Otsu Algorithm Research Articles

A Recognition Method of Soybean Leaf Diseases Based on an Improved Deep Learning Model.

Soybean is an important oil crop and plant protein source, and phenotypic traits' detection for soybean diseases, which seriously restrict yield and quality, is of great significance for soybean breeding, cultivation, and fine management. The recognition accuracy of traditional deep learning models is not high, and the chemical analysis operation process of soybean diseases is time-consuming. In addition, artificial observation and experience judgment are easily affected by subjective factors and difficult to guarantee the accuracy of the objective. Thus, a rapid identification method of soybean diseases was proposed based on a new residual attention network (RANet) model. First, soybean brown leaf spot, soybean frogeye leaf spot, and soybean phyllosticta leaf spot were used as research objects, the OTSU algorithm was adopted to remove the background from the original image. Then, the sample dataset of soybean disease images was expanded by image enhancement technology based on a single leaf image of soybean disease. In addition, a residual attention layer (RAL) was constructed using attention mechanisms and shortcut connections, which further embedded into the residual neural network 18 (ResNet18) model. Finally, a new model of RANet for recognition of soybean diseases was established based on attention mechanism and idea of residuals. The result showed that the average recognition accuracy of soybean leaf diseases was 98.49%, and the F1-value was 98.52 with recognition time of 0.0514 s, which realized an accurate, fast, and efficient recognition model for soybean leaf diseases.

Spatiotemporal Change Detection of Coastal Wetlands Using Multi-Band SAR Coherence and Synergetic Classification

Synthetic aperture radar (SAR) signal can penetrate clouds and some vegetation canopies in all weather, and therefore, provides an important measurement tool for change detection and sustainable development of coastal wetland environments and ecosystems. However, there are a few quantitative estimations about the spatiotemporal coherence change with multi-band SAR images in complex coastal wetland ecosystems of the Yellow River Delta (YRD). In this study, C-band Sentinel-1 and L-band ALOS-2 PALSAR data were used to detect the spatiotemporal distribution and change pattern of interferometric coherence in the coastal wetlands of the YRD. The results show that the temporal baseline has a greater impact on the interferometric coherence than the perpendicular baseline, especially for short wavelength C-band SAR. Furthermore, the OTSU algorithm was proven to be able to distinguish the changing regions. The coherence mean and standard deviation values of different land cover types varied significantly in different seasons, while the minimum and maximum coherence changes occurred in February and August, respectively. In addition, considering three classical machine learning algorithms, namely naive Bayes (NB), random forest (RF), and multilayer perceptron (MLP), we proposed a method of synergetic classification with SAR coherence, backscatter intensity, and optical images for coastal wetland classification. The multilayer perceptron algorithm performs the best in synergetic classification with an overall accuracy of 98.3%, which is superior to a single data source or the other two algorithms. In this article, we provide an alternative cost-effective method for coastal wetland change detection, which contributes to more accurate dynamic land cover classification and to an understanding of the response mechanism of land features to climate change and human activities.

Motion Track Enhancement Method of Sports Video Image Based on OTSU Algorithm

Current sports video image tracking methods cannot get the average gray information of pixels, which leads to the low segmentation accuracy of target trajectory. Therefore, this paper proposes a method of enhancing the motion track of sports video object based on OTSU algorithm. The OTSU algorithm is introduced to segment the unsupervised image automatically based on threshold calculation. Combined with 3D histogram, the OTSU algorithm is optimized to overcome the influence of noise and nonuniform illumination and to improve the segmentation performance. Using the optimized OTSU algorithm, the preprocessing of sports video image is realized by gray level transform, median filter, and binarization. Based on this, the object motion model of sports video image is constructed, and the edge information of sports video image is extracted. Using binocular stereo vision technology, according to the image target motion model and the current space coordinates, calculate the motion track, and obtain the parameters of sports video image. Experimental results show that the proposed method can segment the target accurately in a shorter time, and the enhancement effect of target trajectory is obvious, and the error is small.

Morphological Examination of Vertebral Bodies (Centrum) From Grass Carp (Ctenopharyngodon idella) Using High‐Resolution Micro‐CT Scans

The vertebral column, a defining trait of all vertebrates, is organized as a concatenated chain of vertebrae, and therefore its support to the body depends on individual vertebral morphology. Consequently, studying the morphology of the vertebral body (centrum) is of anatomical and clinical importance. In this study, we have investigated the morphology of Grass carp (Ctenopharyngodon idella) vertebral centrum using high‐resolution micro‐CT scans. Grass carp is a member of the interclass Teleostei (teleost fish), part of the class Actinopterygii, which accounts for the majority of all vertebrate species; thus, Grass carp vertebral morphology can help us understand normal vertebrate vertebral morphology and development. Based on previous studies done on teleost fish, we hypothesized that vertebral centrum will demonstrate bilateral symmetry along the sagittal (left/right) and transverse (cranial/caudal) planes. To this end, three vertebrae (numbers V9‐11) from eight Grass carps that were housed in a Living Stream Systems for 8 weeks at 15⁰C were harvested and micro‐CT scanned at a resolution of 19.29 µm (SkyScan1173, Bruker Inc). The scans were reconstructed into 16‐bit TIFF image stacks and then imported into Dragonfly (ORS Inc.) for segmentation using an Otsu algorithm. After centering each vertebrae along its three principal axes various morphological measurements were performed. Grass carp vertebral centrum demonstrated an hourglass shape where both endplates are concave. While the vertebral centrum demonstrated bilateral symmetry along the sagittal plane (left/right, in line with our hypothesis), vertebral centrum demonstrated deviation from bilateral symmetry along the transverse plane (cranial/caudal, refuting our hypothesis). The vertebral centrum cranial endplate size (perimeter and area) was found to be smaller and less circular compared to the caudal endplate. This implies that the vertebral body tapers off from caudal to cranial. In addition, the cranial endplate was found to be shallower (i.e. more obtuse angles, α > β, see figure 1) compared to the caudal endplate. As a result of the shallower cranial endplate, the center of the vertebrae (narrowest part of the hourglass, between the two endplates) was shifted cranially (A < B, see figure 1). The sum of these findings implies that the caudal aspect of Grass carp vertebral centrum is bigger and more robust. Currently we have no information whether this is due to nature, e.g. differences in gene expression, or nurture, e.g. environmental effect. As the vertebral transverse and spinous processes are slanted caudally, the direction of muscle action during swimming may create a gradient of stresses from cranial to caudal, resulting in a more robust caudal aspect of the vertebral centrum. Expanding our study to include additional quadrupedal and bipedal (i.e. humans) vertebrae, as well as testing if these morphological aspects of the vertebrae are indeed plastic and can be affected by environmental factors (i.e. temperature or other stressors) may help answering this interesting question.

A modified EMD method for detecting and mitigating narrow-band RFI in SAR data

ABSTRACT The empirical mode decomposition (EMD) based method for mitigating radio frequency interference (RFI) has the capability of removing narrow-band RFI and the advantage of easy-to-implement. The signals are decomposed by EMD into a finite number of intrinsic mode functions (IMFs) in a pulse-by-pulse manner, and then the IMFs corresponding to the RFI are subtracted to achieve interference mitigation. However, useful signals will be lost by using this method, which reduces the synthetic aperture radar (SAR) imaging quality. In this letter, a modified EMD method is proposed to tackle this problem. Firstly, the interferences of the echoes are detected through the relative energy ratio. Secondly, echoes containing RFI are decomposed into many IMFs via EMD. These IMFs are transformed into time-frequency (TF) domain via short-time Fourier transform (STFT), and then IMFs corresponding to RFI are screened out via the robust bisecting k-means algorithm. Finally, the RFIs in TF domain are located with the robust OTSU algorithm and mitigated by a notch operation. Experimental results of real SAR data with simulated RFIs demonstrate the effectiveness and superiority of the proposed method.

Optimization of endocrine pancreas fluorescence analysis using machine methods

The study aims to establish the appropriate parameters of UV excitation intensity and permanent excitation time on the pancreatic islets photobleaching, the ratio of the intensity of the useful signal in the region of interest to the intensity of nonspecific background fluorescence. Materials and methods. The pancreas of three adult Wistar rats was fixed in Bouin solution (20 hours) and poured into paraplast after standard histological processing. The study was carried out on paraffin sections of the pancreas. The islets’ insulin and glucagon were determined by immunofluorescence method using monoclonal antibodies (Santa Cruz Biotechnology). The immunofluorescence reaction was studied using an AxioImager-M2 fluorescent microscope. AxioVision digital image analysis system was used for fluorescence imaging, and ImageJ 64-bit image analysis system was used for image quantification. 30 pancreatic islets with an area from 3000 µm2 to 5000 µm2 (8–13 % of the frame area) were analyzed. Results. Measurements carried out at constant values of hormone concentration in endocrinocytes showed a different estimate of the average fluorescence intensity for insulin and glucagon, which depended on the intensity of UV radiation. As the intensity of UV radiation increased, the average fluorescence intensity in the region of interest for insulin and glucagon increased, but when the camera exposure was corrected, it became almost the same. Regardless of this, the intensity of nonspecific background fluorescence increased monotonically. The use of the ratio of the logarithms of the background fluorescence of the drug and the fluorescence of endocrinocytes in the calculations gives a stable estimate of the relative concentration of hormones, which does not depend on the intensity of the selected UV radiation regime, as well as on the duration of UV irradiation of the drug. This makes it possible to neutralize the effect of photodynamic discoloration of the preparation caused by continuous irradiation. Methods for machine selection of the region of interest by various algorithms of the ImageJ program lead to different estimates of its area, integral, and average fluorescence values. At the same time, the result closest to the “ideal” interactive method of highlighting the area of interest for insulin and glucagon was shown by Otsu’s algorithm. Conclusions. In immunofluorescent examination of the pancreas, a moderate UV radiation mode should be selected, exposure correction of the CCD camera before taking each frame, and the total time for examining the visual field of the sample should be limited to 1–2 minutes. To highlight the area of interest for insulin and glucagon in automatic analysis, it is recommended to use the Otsu algorithm. To obtain a quantitative estimate of the average fluorescence intensity in the region of interest, it is recommended to use the ratio of the logarithms of the background fluorescence of the drug and endocrinocytes in the calculations.

Disturbed boundaries extraction in coal-grain overlap areas with high groundwater levels using UAV-based visible and multispectral imagery.

With high groundwater levels, coal-grain overlap areas (CGOAs) are vulnerable to subsidence and water logging during mining activities, thereby impacting crop yields adversely. Such damage requires full reports of disturbed boundaries for agricultural reimbursement and ongoing reclamation, but because direct measurements are difficult in such cases because of vast unreachable areas, it is necessary to be able to identify out-of-production boundaries (OBs) and reduced-production boundaries (RBs) in the corresponding region. In this study, an OB was extracted by setting a threshold via the characteristics of the cultivated-land elevation based on a digital surface model and a digital orthophoto map generated using an unmanned aerial vehicle (UAV). Meanwhile, the above-ground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI) were used to select the appropriate vegetation indices (VIs) to produce a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR), and random forest (RF) algorithms. Finally, an improved Otsu segmentation algorithm was used to extract mild and severe RBs. The results showed the following. (1) Crop growth heights in a typical ponding basin of the CGOA rendered a fast and efficient approach to distinguishing the OB. (2) In subsequent sample modeling, the red-edge microwave VI (MVIredge), the normalized difference VI (NDVI), and the red-edge modified simple ratio index (MSRredge) combined with RF were shown to be optimal estimators for AGB (R2 = 0.83, RMSE = 0.114kg·m-2); the red-edge NDVI (NDVIredge), the green NDVI (GNDVI), and the red-edge chlorophyll index (CIredge) acted as strong tools in SPAD prediction using RF (R2 = 0.83, RMSE = 0.152 SPAD); the red-edge modified simple ratio index (MSRredge), the GNDVI, and the green chlorophyll index (CIgreen) via MR were more accurate when conducting the inversion of LAI (R2 = 0.88, RMSE = 1.070). (3) With the improved Otsu algorithm, multiple degrees of RB extraction can be achieved in RM. This study provides reference methods and theoretical support for determining disturbed boundaries in CGOAs with high groundwater levels for further agricultural compensation and reclamation processes.

Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning

Urban flooding poses risks to the safety of drivers and pedestrians, and damages infrastructures and lifelines. It is important to accommodate cities and local agencies with enhanced rapid flood detection skills and tools to better understand how much flooding a region may experience at a certain period of time. This results in flood management orders being announced in a timely manner, allowing residents and drivers to preemptively avoid flooded areas. This research combines information received from ground observed data derived from road closure reports from the police department, with remotely sensed satellite imagery to develop and train machine-learning models for flood detection for the City of San Diego, CA, USA. For this purpose, flooding information are extracted from Sentinel 1 satellite imagery and fed into various supervised and unsupervised machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classifier (MLC), to detect flooded pixels in images and evaluate the performance of these ML models. Moreover, a new unsupervised machine learning framework is developed which works based on the change detection (CD) approach and combines the Otsu algorithm, fuzzy rules, and iso-clustering methods for urban flood detection. Results from the performance evaluation of RF, SVM, MLC and CD models show 0.53, 0.85, 0.75 and 0.81 precision measures, 0.9, 0.85, 0.85 and 0.9 for recall values, 0.67, 0.85, 0.79 and 0.85 for the F1-score, and 0.69, 0.87, 0.83 and 0.87 for the accuracy measure, respectively, for each model. In conclusion, the new unsupervised flood image classification and detection method offers better performance with the least required data and computational time for enhanced rapid flood mapping. This systematic approach will be potentially useful for other cities at risk of urban flooding, and hopefully for detecting nuisance floods, by using satellite images and reducing the flood risk of transportation design and urban infrastructure planning.

Research on Identification Method for Interface Flange in Automatic Docking System of Fluid Loading and Unloading Arm for Bottom Loading

The automatic docking system of the loading and offloading arm of a tank car is the key link to realizing the unmanned operation of tank car loading and unloading. The spatial position detection of the flange port of a tank car can guide the automatic docking of the fluid loading and offloading arm and flange port of the tank car. In this paper, a flange position detection method based on image recognition was proposed. Firstly, the end state of the loading arm was analyzed to determine the expression mode of the loading arm’s spatial pose so as to form a unified expression with the flange position and docked pose on the tank car. Then, for the image processing of the flange port of the tank car, this paper binarized the edge of the flange end face based on the Otsu algorithm, used the Canny algorithm for edge detection, used the least squares method to fit the image edge coordinates into a spatial circle, calculated the center coordinates and normal vector of the flange end face, and used these parameters to guide the end of the loading arm to adjust the position and attitude so that it was consistent with the position and pose of the flange port to realize docking. Then, a circular object center detection and calibration experiment, a flange end face image experiment, and an automobile tank car flange port physical detection experiment were carried out. The test results show that the spatial coordinate accuracy of the flange port diameter and center detected by this method meets the requirements of the loading arm automatic docking system, providing a research idea for the design of an automatic docking system for the loading and unloading arm of a tank car.

Improved Spectral Water Index Combined with Otsu Algorithm to Extract Muddy Coastline Data

Based on the spectral reflection characteristics analysis of the muddy coastline in Jiangsu, an improved spectral water index (IWI) combined with the Otsu algorithm is proposed to extract muddy coastlines from Landsat Operational Land Imager (OLI) images. The IWI-extracted coastline results are compared with those extracted by the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), enhanced water index (EWI), revised normalized different water index (RNDWI) and automated water extraction index (AWEI). The results show that the IWI is not affected by tidal conditions or sand content in the water, can reduce the “salt-and-pepper” phenomenon in the Otsu algorithm classification, can accurately identify water boundaries and can extract silty mudflats and marine buildings with high accuracy. It can also significantly increase the degree of automation of coastline extraction. The IWI combined with the Otsu algorithm demonstrates high accuracy of over 84% in the extraction muddy coastline data with one-pixel tolerance, which is twice as accurate as other indices. The accuracy of extraction for all other types of coastlines is over 81%. Therefore, the IWI index combined with the Otsu algorithm is reliable for studies of sea–land processes and coastline evolutions.

Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India

The aquaculture expansion has made significant contributions to global food security, socio-economic development and, if implemented sustainably, can help preserve stable coastal environments. This study explicitly details the rapid expansion of large-scale aquaculture growth across the Kolleru and Upputeru regions of South India. We developed a novel classification method for automated extraction of aquaculture ponds in the Kolleru zone using the Canny Edge-Otsu algorithm to segment and extract the ponds applied to SAR-VV images in Google Earth Engine. This approach enables the area estimation of dense aquaculture ponds are essential for monitoring and management purposes. The results indicated that this method could effectively map the aquaculture ponds and the overall accuracy achieved in 2020 for the Kolleru and Upputeru areas by 90.6% and 95.7%, respectively. The aquaculture maps of this study can help government organizations, resource managers, stakeholders, and decision-makers understand the dynamics and plan sustainable measures in this area.

Fast Location of Table Grapes Picking Point Based on Infrared Tube

This study investigates the low mechanization level of grape picking, and the problems associated with the difficult location of grape picking points in three-dimensional space. A method for rapidly locating the picking points of table grapes based on an infrared tube was proposed in this paper. Firstly, the Otsu algorithm and the maximum connected domain were used to obtain the image of the target grape, which realized the fast recognition and segmentation of the target grape in two-dimensional space. Secondly, a location device for grape-picking points based on an infrared tube was designed, which resolved the technical problem related to the difficulty of locating picking points in three-dimensional space, and realized the accurate positioning of picking points for table grapes. Finally, the experimental results show that the proposed method can quickly and accurately locate the picking points of table grapes in three-dimensional space. The average running time of the proposed algorithm is 0.61 s, and the success rate of location identification is 90.0%. It provides a feasible scheme for the mechanized picking of table grapes.

Exudate Extraction From Fundus Images Using Machine Learning

Patients suffering from diabetes have to bear several other disorders due to this. Diabetic Retinopathy is one such disorder which affects diabetic patients. This disorder affects the patient’s eye leading to permanent blindness if left untreated. Another disorder is exudates in which lipid residues leak out from damaged capillaries. It appears as yellow flecks. Hard exudates can lead to life threatening disorders. Detecting Hard exudates help the Ophthalmologist to diagnose the severity of the patient’s condition and in turn help in better medication. This paper presents a method to adjust the contrast of the image which in turn helps in detecting the hard exudates which can be used for further processing. In this work, initially Otsu algorithm is applied and then compared with Machine Learning techniques due to the disadvantage of Otsu.

Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information

Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.

Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation.

The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the Particle Swarm Optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury.

The study focused on the therapeutic effects of high-flow oxygen therapy on patients with critical lung injury using edge detection-based ultrasound images. Firstly, the traditional Canny edge detection algorithm was improved, and the optimal threshold was obtained by optimizing the median filter and combining Otsu algorithm and threshold iteration method. Then, the optimized algorithm was compared with the traditional Canny edge detection algorithm and applied to process the lung ultrasound images of 120 cases of critical lung injury, to compare the efficacy of high-flow oxygen therapy and the traditional oxygen therapy. It was found that the peak signal-to-noise ratio (PSNR) (20.34~31.3), edge intensity value (17.89~27.34), and edge detection effect of the improved Canny algorithm were better than the traditional Canny algorithm (15.2~28.61, 9.44~18.56). The failure rate of extubation (4.1%), reintubation rate (0.8%), comfort (2.38 ± 0.15 points), dry humidity score (1.07 ± 0.21 points), antibiotic use (7.41 ± 0.74 days), and hospital stay (8.66 ± 1.02 days) in the experimental group were significantly lower than the corresponding indexes in the control group (11.7%, 5%, 4.25 ± 0.26 minutes, 4.94 ± 0.78 minutes, 19.29 ± 1.7 days, and 27.49 ± 2.22 days), and the difference was statistically significant (P < 0.05). In the experimental group, within 48 hours after extubation, the respiratory rate (RR), heart rate (HR), arterial partial pressure of carbon dioxide (PaCO2), and HCO3− were significantly lower than those of the control group; and the values of transcutaneous oxygen saturation (SpO2), mean arterial pressure (MAP), arterial partial pressure of oxygen (PaO2), and pH were significantly higher than the control group, and the difference was statistically significant (P < 0.05). In conclusion, the algorithm in this study is superior to the traditional Canny algorithm, and the high-flow oxygen therapy can reduce the failure rate of extubation, strengthen patient comfort, improve the degree of gas humidification, stabilize the respiratory function and circulatory system, and shorten the time of antibiotic use and hospital stay.

Study on the non-linear deformation and failure characteristics of EPS concrete based on CT-scanned structure modelling and cloud computing

To understand the complex deformation features and failure mechanisms of expanded polystyrene (EPS) concrete and reveal the composite effect of expanded polystyrene beads and polypropylene fibers, a series of experiments were conducted on the poured EPS concrete specimens. Meanwhile, a cloud computing system for 3D realistic failure process analysis (RFPA3D) was established to model the fine failure process of a real concrete structure. The micromorphology of the EPS concrete specimens was obtained via CT scanning and further processed using digital image processing technology. The Otsu algorithm was applied to automatically recognize the segmentation thresholds of each partition image and a procedure for CT image processing was designed to automatically realize digital image segmentation and merging. Then, the numerical models reflecting the microstructures of the EPS concrete specimens were built using the processed digital images and a series of 3D numerical simulations were performed using cloud-computing-based RFPA3D. The results show that for concrete with low EPS volume fracture, the non-smooth convex-step-shaped failure morphology, which is a typical brittle fracture characteristic, appears. In contrast, ductile fracture occurs for concrete with a high EPS volume fracture. Simultaneously, the addition of polypropylene fibers of a certain length can effectively prevent the formation and expansion of new cracks in the cement matrix. In addition, the peak strength of concrete increases with an increase in homogeneity while the residual strength generally decreases with an increase in homogeneity. Moreover, a more heterogeneous material presented more acoustic emission precursors before macro fracture. All these achievements greatly improve our knowledge of the design, construction, and maintenance of EPS concrete in civil engineering.

On-Orbit Spatial Quality Evaluation of SDGSAT-1 Thermal Infrared Spectrometer

The Sustainable Development Science Satellite (SDGSAT-1) was successfully launched on 5th November 2021. It is the world&#x2019;s first scientific satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development. To evaluate the on-orbit image quality of its thermal infrared spectrometer (TIS), we propose an improved edge slope calculation method. Learning from past academic research, we found that the traditional edge slop (ES) calculation methods are limited by various factors. For example, it demands highly on geographical aspects and surface temperature uniformity. In this paper, we interpolate the narrow sea-land boundary region. The edge signal-to-noise ratio and dip angle are calculated by the selected region to determine whether it satisfies the conditions of ES calculation. The Otsu algorithm is used to find the optimal threshold for the sea-land division, and the sub-pixel edge positions are extracted by combining with the canny operator. In order to achieve higher precision, we use the modified Fermi function to fit the extracted edge spread function (ESF). During the on-orbit testing of the SDGSAT-1/TIS, we performed ES calculations for a large number of test areas with a flight direction of 0.5 and a scan direction of 0.45.

Retracted] The Application of the Internet of Things Framework in the Ideological and Political Teaching System

This study uses Internet of Things technology to build a smart system for ideological and political education in order to remove several flaws in present ideological and political teaching. Furthermore, to achieve intelligent monitoring of the ideological and political classroom, this research uses a technique based on IGHT edge feature matching and the ideological and political classroom monitoring image monitoring calculation formula to compute the image light reflection angle. In addition, this paper adopts the method of adaptively obtaining segmentation threshold (OTSU algorithm) to binarize the differenced image. After constructing the smart teaching system, this paper conducts a systematic evaluation based on the simulation experiment. The research results show that the ideological and political teaching system based on the Internet of Things technology proposed in this paper has a good simulation teaching effect, and the system can be used in the follow‐up ideological and political teaching to improve the teaching effect.

Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior

The intensity of some pixels of the captured fringe will be saturated when fringe projection profilometry is used to measure objects with high reflectivity, which will significantly affect the reconstruction of the measured object. In this paper, we propose a fringe pattern inpainting method based on the convolutional neural network (CNN) denoiser prior guided by additional information from a fringe captured in short exposure time. First, a binary mask obtained by Otsu algorithm from the modulation information of the short exposure fringe is used to detect the high-saturation region in the normal exposure fringe. Then, the corrected gray-scales of the region of the short exposure fringe selected by the mask are inserted in the saturated region of the normal fringe to form an initial fringe for iteration. At last, fringe pattern inpainting is achieved by using a CNN denoiser prior. The correct phase can be reconstructed from the inpainted fringes. The computer simulation and experiments verify the effectiveness of the proposed method.